October 8, 2018 at 10:00 a.m. (MT)
Title of Thesis: Identifying Malicious VoIP Usage using Computational Intelligence
Co-Supervisor: Dr. Mahmoud Abaza
Mode of Delivery: Adobe Connect
VoIP user accounts are a prime target for hackers to compromise for profit. VoIP accounts are targets of the same types of attacks as any other Internet account that is authorized with a username and password. Unlike many other Internet accounts VoIP has a direct monetary cost to the user being compromised. Toll-fraud perpetrated using a compromised VoIP account can accrue expensive toll-charges that either the user or the service provider are liable to pay for. This paper discusses the prior research in detecting unauthorized usage on VoIP accounts. The researched methods are based on machine learning techniques. A new technique of using a Recurrent Neural Network for detecting unauthorized usage periods on a VoIP account is developed and demonstrated. The technique uses a Long-Short Term Memory style of Recurrent Neural Network to achieve over a 99% accuracy when testing against calls tagged as occurring during a toll-fraud event.
Updated October 02 2018 by Student & Academic Services